Nearly-optimal compression matrices for signal power estimation

Daniel Romero, Roberto Lopez-Valcarce

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

We present designs for compression matrices minimizing the Cramér-Rao bound for estimating the power of a stationary Gaussian process, whose second-order statistics are known up to a scaling factor, in the presence of (possibly colored) Gaussian noise. For known noise power, optimum designs can be found assuming either low or high signal-to-noise ratio (SNR). In both cases the optimal schemes sample the frequency bins with highest SNR, suggesting near-optimality for all SNR values. In the case of unknown noise power, optimal patterns in both SNR regimes sample two subsets of frequency bins with lowest and highest SNR, which also suggests that they are nearly-optimal for all SNR values.

Original languageEnglish (US)
Pages434-438
Number of pages5
DOIs
StatePublished - Oct 31 2014
Event2014 15th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2014 - Toronto, Canada
Duration: Jun 22 2014Jun 25 2014

Other

Other2014 15th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2014
CountryCanada
CityToronto
Period6/22/146/25/14

Keywords

  • Compressive covariance sensing
  • power estimation
  • sampler design
  • spectrum sensing

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